{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/farsa-fully-automated-roadway-safety","title":"FARSA: Fully Automated Roadway Safety Assessment","arxiv_id":"1901.06013","date":"2019-01-17","proceeding":null,"authors":["Weilian Song","Scott Workman","Armin Hadzic","Xu Zhang","Eric Green","Mei Chen","Reginald Souleyrette","Nathan Jacobs"],"abstract":"This paper addresses the task of road safety assessment. An emerging approach\nfor conducting such assessments in the United States is through the US Road\nAssessment Program (usRAP), which rates roads from highest risk (1 star) to\nlowest (5 stars). Obtaining these ratings requires manual, fine-grained\nlabeling of roadway features in street-level panoramas, a slow and costly\nprocess. We propose to automate this process using a deep convolutional neural\nnetwork that directly estimates the star rating from a street-level panorama,\nrequiring milliseconds per image at test time. Our network also estimates many\nother road-level attributes, including curvature, roadside hazards, and the\ntype of median. To support this, we incorporate task-specific attention layers\nso the network can focus on the panorama regions that are most useful for a\nparticular task. We evaluated our approach on a large dataset of real-world\nimages from two US states. We found that incorporating additional tasks, and\nusing a semi-supervised training approach, significantly reduced overfitting\nproblems, allowed us to optimize more layers of the network, and resulted in\nhigher accuracy.","url_abs":"http://arxiv.org/abs/1901.06013v1","url_pdf":"http://arxiv.org/pdf/1901.06013v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"farsa-fully-automated-roadway-safety","repo_url":"https://github.com/arminHadzic/Panorama_Valhalla","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}